Adversarial training succeeds in generating and restoring visually realistic images [1]}, [2]}, [3]}.
On video deblurring, Zhang [4]} uses a discriminator to distinguish between the restored single frame and its ground-truth, which does not consider temporal constraints.
In contrast, we exploit the proposed progressive training scheme and design an adversarial loss to encourage models to output temporally-consistent results.
In this regard, we propose a temporal discriminator \(\mathcal {D}_{\zeta }\) , parameterized by \(\zeta \) , that distinguishes \(\vec{\mathbf {R}}_i=\lbrace \mathbf {R}_{i-1}^1\,\mathbf {R}_{i}^2,\,\mathbf {R}_{i+1}^2\rbrace \)  from their ground-truth sharp images \(\vec{\mathbf {S}}_i=\lbrace \mathbf {S}_{i-1},\,\mathbf {S}_{i},\,\mathbf {S}_{i+1}\rbrace \) .
In specific, \(\mathcal {D}_\zeta \)  consists of four 3D convolution layers followed by a global pooling to extract temporal features for the ground-truth and restored frame sequences. It then predicts whether the sequence is a ground-truth sequence or not using a binary classifier.
The adversarial loss is defined as follows,
\(L_{adv}=\mathbb {E}_\xi [\log (D_\zeta (\vec{\mathbf {S}}))] + \mathbb {E}_\chi [\log (1-D_\zeta (\vec{\mathbf {R}}))],\) 
